Textbook:Spatial Econometrics using Microdata (GIS and Territorial Intelligence)
Author(s): Diègo Legros

Description:
This textbook provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data.
Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency.
then it focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares approach.


The books is split into six chapters that follow a precise logic. Chapter 1 proposes an introduction to spatial analysis related to disaggregated or individual data (spatial microdata). Particular attention is placed on the structure of spatial databases as well as their particularities. It shows why it is essential to take account of the spatial dimension in econometrics if the researcher has data that is geolocalized; it presents a brief history of the development of the branch of spatial econometrics since its formation.
Chapter 2 is definitely the central piece of the work and spatial econometrics. It serves as an opening for the other chapters, which use weights matrices in their calculations. Therefore, it is crucial and it is the reason for which particular emphasis is placed on it with many examples. A fictional example is developed and taken up again in Chapter 3 to demonstrate the calculation of the detection indices of the spatial autocorrelation patterns.

Chapter 3 presents the most commonly used measurements to detect the presence of spatial patterns in the distribution of a given variable. These measurements prove to be particularly crucial to verify the assumption of the absence of spatial correlation between the residuals or error terms of the regression model. The presence of a spatial autocorrelation violates one of the assumptions that ensures the consistency of the estimator of the ordinary least squares (OLS) and can modify the conclusions coming from the statistical model. The detection of such a spatial pattern requires the correction of the regression model and the use of spatial and spatio-temporal regression models. Obviously, the detection indices can also be used as descriptive tools and this chapter is largely based on this fact.
Chapters 4 and 5 present the autoregressive models used in spatial econometrics. The spatial autoregressive models (Chapter 4) can easily be transposed to spatio-temporal applications (Chapter 5) by developing an adapted weights matrix to the analyzed reality. A particular emphasis is put on the intuition behind the use of one type of model rather than another: this is the fundamental idea behind the DGP. In function of the postulated model, the consequences of the spatial relation detected between the residuals of the regression model can be more or less important, going from an imprecision in the calculation of the estimated variance, to a bias in the estimations of the parameters. The appendixes linked to Chapters 4 (spatial modeling) and 5 (spatio-temporal modeling) are based on the simulation of a given DGP and the estimation of autoregressive models from the weights matrices built previously (see Chapter 2).
Finally, the Conclusion is proposed, underlying the central role of the construction of the spatial weights matrix in spatial econometrics and the different possible paths allowing the transposition of existing techniques and methods to different definitions of the “distance”.